Chapter 4:
Pilot Study Data Analysis and Results(Continued)

Results of Crash Analyses

Motor Free Visual Perception Visual Closure Subtest (MVPT/VC). Figure 31 contains the
results for the MVPT/VC. The top plot relates functional performance to crash involvement, using
"all crashes" as the safety outcome measure. The middle plot relates functional status to the more restrictive outcome measure of "at-fault and unknown fault" crashes, and the bottom plot shows the
distributions of License Renewal sample drivers with and without "at-fault" crashes at each level of
functional ability measured by this test. In all cases, declining functional ability is indicated by an
increasing number of incorrect responses, moving to the right along the x-axis.

Figure 31.
MVPT/VC Performance Distributions and Odds Ratios for Analyses
Including All Crashes, At-Fault and Unknown-Fault Crashes,
and At-Fault Crashes
Only

Inspection of this figure reveals stronger relationships moving from the top to the bottom data
plot; this is associated with a progressive increase in the peak OR value from 2.21 for "all crashes"
to 4.96 for "at-fault" crashes only. The peak OR (4.96), associated with a cutpoint of 5 incorrect
responses, is statistically significant (c2 = 26.48, df=1, p<.001). It is also interesting to note that,
in all three plots the proportion of drivers who are crash-involved begins to exceed the proportion
who are crash-free at the same level of functional performance--four incorrect responses.

Finally, it may be observed that the distributions of crash-involved drivers appears bimodal,
especially for at-fault crashes, while the percentages of non-crashing drivers falls off in a linear
fashion with declining functional ability.

The data plotted in figure 31 are presented in tables 27, 28, and 29 of appendix F. The chi-square test results noted above, with corresponding cell counts, can be found in table 48 of appendix
G.

Delayed Recall. Figure 32 shows the relationships between performance on the Delayed
Recall procedure and the three indices of crash involvement analyzed here.

Figure 32.
Delayed Recall Performance Distributions and Odds Ratios for
Analyses
Including All Crashes, At-Fault and Unknown-Fault Crashes,
and At-Fault
Crashes Only

As shown, the association between functional performance and crash involvement, revealed
through calculated OR values at each of the four possible levels for this measure, indicates elevated
crash risk with a greater loss of working memory. The association is progressively stronger moving
from "all crashes" to "at-fault" crashes only. In the latter case, for drivers who missed all 3 items
crash risk was elevated by 2.92 times, which was statistically significant at p<.02 (c2 = 5.25, df=1).
At the same time, the proportion of the sample who were crash involved began to exceed those who
were crash free at the level of two incorrect responses, suggesting this as a potential cutpoint for this
measure.

The data plotted in figure 32 are presented in tables 30, 31, and 32 of appendix F. The chi-square test results noted above, with corresponding cell counts, can be found in table 49 of appendix
G.

Useful Field of View, Subtest 2. Figure 33 contains the results for the Useful Field of View,
Subtest 2. The plots in this figure allow comparison of the distributions of crash-involved and non-crash-involved drivers at each target duration for this measure. It may be noted that poorer
performance is signified when drivers need longer durations to correctly identify the target; and, each
value on the x-axis is actually the midpoint of a 50 msec interval.

Figure 33.
Useful Field of View, Subset 2 Performance Distributions and Odds,
Ratios for Analyses Including All Crashes, At-Fault and Unknown-Fault Crashes,
and At-Fault Crashes Only

While the performance level at which the proportion of crash-involved drivers exceeds non-crash-involved drivers is 250 msec, the peak OR of 2.48 for this measure obtains at a slightly longer
duration, 300 milliseconds. The calculated OR is statistically significant (c2 = 6.95, df=1, p<.01)
at the latter cutpoint (which is an interval with lower boundary set at 275 msec).

Though less pronounced than MVPT/VC, the plots for Subtest 2 of the Useful Field of View
measure also suggest a multimodal shape for the crash-involved group, most noticeably for at-fault
crashes. Interpretation is complicated by the spike at 500 msec; as noted earlier, this is an artifact
of the measurement technique, inasmuch as all responses at this target duration and longer were
coded with the same value.

The data plotted in figure 33 are presented in tables 33, 34, and 35 of appendix F. The chi-square test results noted above, with corresponding cell counts, can be found in table 50 of appendix
G.

Trail-Making, Part B. The results for this paper-and-pencil test of perceptual-cognitive ability
are displayed in figure 34. As observed in the related, Useful Field of View (Subtest 2) plots
displayed previously, the curve relating safety outcome to functional status is essentially flat using
"all crashes." Also, the values on the x-axis in this figure are again actually the midpoints of
intervals; each interval is 40 msec long.

Figure 34.
Trail-Making, Part B Performance Distributions and Odds Ratios
for
Analyses Including All Crashes, At-Fault and Unknown-Fault Crashes,
and At-Fault
Crashes Only

A strong consistency observed in these data is that the proportion of drivers in the sample
who were crash-involved began to exceed those who were crash free at the 100 second performance
level, across all crash categories. This suggests that 100 seconds may be the best candidate for a
cutpoint on this screening measure.

The results reported in the middle plot show a somewhat stronger association overall but do
not show any clear peak for the calculated OR. It isn't until the bottom plot for at-fault crashes that
the OR shows a clear peak (3.50) at the 100 second level. Drivers were over 3½ times more likely
to be involved in an at-fault crash if their score was 80 seconds (i.e., the lower bound of this analysis
interval) or longer on this measure, a statistically significant outcome (c2 = 7.72, df=1, p<.01).

The data plotted in figure 34 are presented in tables 36, 37, and 38 of appendix F. The chi-square test results noted above, with corresponding cell counts, can be found in table 51 of appendix
G.

Dynamic Trails. Figure 35 plots the results for Dynamic Trails. This automated test was
related to the paper-and-pencil Trail-making (Part B) measure but was shorter, with fewer test items,
and also potentially more distracting, with moving traffic in the background instead of a blank page.

Figure 35.
Dynamic Trails Performance Distributions and Odds Ratios for
Analyses
Including All Crashes, At-Fault and Unknown-Fault Crashes,
and At-Fault
Crashes Only

A peak valid OR of 1.45 was calculated for this measure at a test completion time of 25
seconds, for the "at-fault" crash category. This outcome was not statistically significant (c2 = .57,
n.s.). In part, this outcome may reflect the fact that the sample size (n = 777) for this particular
measure was only about half of that attained for the other procedures in the screening battery. Also,
as reported anecdotally by test administrators at the MVA field data collection sites, participants had
the greatest difficulty understanding the instructions on how to perform this procedure.

To the extent justified by data collection with a larger study sample, choosing a candidate
cutpoint for this measure is problematic. At 20 seconds, the percentage of crash-involved drivers
first exceeded crash-free drivers in the analyses of at-fault crashes; but the largest differentials
between the two distributions were observed at a test completion time of 30 seconds, for all crash
categories.

The data plotted in figure 35 are presented in tables 39, 40, and 41of appendix F. The chi-square test results noted above, with corresponding cell counts, can be found in table 53 of appendix
G.

Scan Test. The remaining measure of perceptual ability, the Scan Test, was scored simply
on a pass/fail basis. With only one criterion possible, OR calculation is irrelevant to cutpoint
determination.

For this measure, 95.6 percent of all drivers in the analysis sample--whether crash-involved
or not--passed. Whether this was due to insensitivity of the measurement procedure or whether
these results reflect a true measurement of generally "intact" functional ability is unclear. Either
way, the very small percentage of drivers failing the measure precludes reliable estimates of
statistical significance. Specifically, the sample would have to be much larger, and/or the criterion
to pass the test more stringent and more consistently implemented, to obtain a reliable cell count of
drivers with at least one crash who failed the test (see earlier discussion of assumptions and
limitations of the odds ratio technique).

Rapid Pace Walk. Figure 36 presents the plots for the Rapid Pace Walk measure. Again,
a pattern of results is shown where the relationship between safety outcome and functional status
appears progressively stronger moving from "all crashes" to "at-fault" crashes.

Figure 36.
Rapid Pace Walk Performance Distributions and Odds Ratios
for
Analyses Including All Crashes, At-Fault and Unknown-Fault Crashes,
and At-Fault
Crashes Only

A statistically-significant (c2 = 6.11, df=1, p<.01) peak OR value of 2.64 was calculated for
this analysis, for the "at-fault" crash category, at the performance level designated 9.75 seconds. A
second peak appears in this plot at the shorter time of 5.25 seconds, however, showing evidence of
the same type of bimodal distribution of functional performance scores among crash-involved drivers
that was observed earlier for MVPT/VC (while the crash-free driver distribution remains linear).

As in the earlier timed measures, each value on the x-axis is the midpoint of an interval; in
this case each interval is 1.5 seconds long. Thus the two values noted above connote analysis
intervals that begin at 9.0 and 4.5 seconds, respectively. The data plotted in figure 36 are presented
in tables 42, 43, and 44 of appendix F. The chi-square test results and cell counts can be found in
table 53 of appendix G.

Foot Tap. Data plots of the results of the Foot Tap measure are presented in figure 37. Each
value on the x-axis is actually the midpoint of a 1.5 second analysis interval.

Figure 37.
Foot Tap Performance Distributions and Odds Ratios
for
Analyses Including All Crashes, At-Fault and Unknown-Fault Crashes,
and At-Fault
Crashes Only

As shown, there is a tendency toward higher OR's at faster times, which was somewhat
unexpected. Also apparent in figure 37 is a close overlap in the distributions of crash-involved and
non-crash-involved drivers, in all three plots. As a result, there are no statistically-significant
differences here, even at the peak OR value of 1.50 calculated at the performance level designated
5.25 seconds in the analysis of "at-fault" crashes (c2 = 0.98, n.s.).

The data plotted in figure 37 are presented in tables 45, 46, and 47 of appendix F. The chi-square test results and cell counts can be found in table 54 of appendix G.

Head/Neck Rotation. As another binary (pass/fail) measure, no OR plots were generated for
Head/Neck Rotation. Sufficient differences were found to support reliable analyses, however: 36.4
percent of drivers with 1 or more (at-fault) crashes failed this test versus only 18.2 percent of drivers
in the non-crash group. The peak OR value of 2.56 for this analysis category was statistically
significant (c2 = 4.69, df = 1, p<.03).

The chi-square test results and cell counts for this measure can be found in table 55 of
appendix G.

Arm Reach. As with the Scan Test measure, virtually all (99.3 percent) of the drivers in the
sample passed the Arm Reach test. Of those who failed, only one driver was involved in an at-fault
crash. The lack of drivers failing this measure precluded reliable statistical tests, and renders this
procedure of little value as a screening tool.

This section quantifies and tests the significance of the statistical relationships between the
functional screening measures and the conviction data extracted from the Maryland Motor Vehicle
Administration files. These associations were calculated according to the conventions for measuring,
sorting, and summarizing functional status and safety outcome data described previously in this
report. A brief overview of the analysis technique follows.

Analysis Techniques

The strength of relationship between functional status and conviction experience was again
assessed through the use of the "odds ratio" (OR) calculation. Greater detail about the nature of this
calculation and the assumptions that must be met for its valid application were provided at the
beginning of the preceding (crash analysis) section.

Results of the OR calculations are indicated in data plots for each functional screening
measure used in the Pilot Study. Each plot shows the percentage of the distribution of drivers in the
License Renewal sample who would fail a test, at each possible cutpoint, that were convicted of
moving violations versus violation-free; and, it shows the calculated OR value at each possible
cutpoint.

In accordance with assumptions and limitations of the OR technique explained earlier, a line
representing the calculated OR value begins at the second-best level of performance, or first possible
cutpoint, marked along the x-axis in each plot presented in this section. Also, in every plot a dashed
line, connoting an OR of 1.0, is included for reference. At this level, a driver is as likely to be crash-involved when passing a test as he/she is when failing the test; and the OR effectively has no
predictive value. Exact OR values for the data represented in the plots, including each potential
cutpoint marked on the x-axis, are presented in appendix I.

Three categories of conviction data are represented in the plots presented in this section: all
moving violations; all moving violations except speeding; and, all moving violations except
speeding and occupant restraint citations. A variety of specific incident types are subsumed under
the heading "moving violations;" these were identified earlier in the section describing the extraction
of motor vehicle administration safety data.

Levels of significance of calculated OR values were assessed using chi-square (c2) tests. Test
statistics were calculated by SPSS/SYSTAT for each functional performance measure where the
strongest relationship with a safety outcome--indicated by the peak valid OR--was demonstrated;
in all cases but one, this outcome was moving violations except speeding and restraint citations. As
a general finding, it was observed that an OR value of approximately 2, or greater, was associated
with a statistically significant (p<.05) chi-square test result.

Results of Conviction Analyses

Motor Free Visual Perception Visual Closure Subtest (MVPT/VC). Figure 38 contains the
results for the MVPT/VC. The top plot relates functional performance to conviction experience
using "all moving violations" as the safety outcome measure. The middle plot relates functional
status to the more restrictive outcome measure of "moving violations without speeding," and the
bottom plot shows the distributions of License Renewal sample drivers with and without moving
violations excluding speeding and occupant restraint citations at each level of functional ability
measured by this test. In all cases, declining functional ability is indicated by an increasing number
of incorrect responses, moving to the right along the x-axis.

Inspection of this figure reveals stronger relationships moving from the top plot, where the
OR curve is virtually flat with calculated values all near 1.0, to the bottom data plot where a
statistically significant (c2 = 10.83, df=1, p<.001) odds ratio of 4.53 was found. The cutpoint where
this result was obtained was at a performance level of six incorrect responses. As shown in figure
38, a higher OR value was calculated for seven incorrect responses, but cell counts were too small
for this calculation to be valid.

A consistent result that also is shown by this figure is the pattern in the relative percentages
of drivers in the distribution with violations versus the percentage who were violation-free. In all
three plots, there is a reversal at the performance level of three incorrect responses; otherwise, at
every level of this measure except perfect performance (zero errors) more drivers who "failed" the
test at a given cutpoint had moving violations than the number who remained violation-free.

The data plotted in figure 38 are presented in tables 56, 57, and 58 of appendix H. The chi-square test results noted above and cell counts can be found in table 77 of appendix I.

Delayed Recall. The relationships between performance on the Delayed Recall procedure
and the three categories of moving violations are described by the plots shown in figure 39.

As shown, the association between functional status and moving violations, revealed through
calculated OR values at each of the four possible levels for this measure, is generally weak. The
peak valid OR, calculated for data described by the bottom plot, was 1.72. This result was obtained
at the level of two incorrect responses; it approached but did not reach statistical significance (c2
= 1.58, n.s.).

The data plotted in figure 39 are presented in tables 59, 60, and 61 of appendix H. The chi-square test results noted above, with corresponding cell counts, can be found in table 78 of appendix
I.

Useful Field of View, Subtest 2. Figure 40 contains the results for the Useful Field of View,
Subtest 2. The plots in this figure allow comparison of the distributions of drivers with and without
moving violations at each target duration characterizing different performance levels for this
measure. As noted earlier in the crash analysis section, all responses at target durations longer than
500 msec were grouped together at that performance level.

Figure 40.
Useful Field of View, Subset 2 Distributions and Odd Ratios for
Analyses
Including All Moving Violations, Moving Violations without Speeding,
and Moving Violations without Speeding and Occupant Restraint Citations

As shown in this figure, OR values hover near 1.0 at all performance levels, for all analysis
categories, with almost exactly matching distributions of drivers with and without moving violations
at each cutpoint. The peak valid OR calculated for Useful Field of View, Subtest 2 was 1.67; this
result obtained at the target duration designated 100 msec in the analysis of "moving violations
except speeding and occupant restraint citations." This result was not statistically significant (c2 =
1.53, n.s.).

The data plotted in figure 40 are presented in tables 62, 63, and 64 of appendix H. The chi-square test results noted above, with corresponding cell counts, can be found in table 79 of appendix
I.

Trail-making, Part B. The results for this paper-and-pencil test of perceptual-cognitive ability
are displayed in figure 41. After MVPT/VC, this measure evidenced the strongest relationship of functional ability with moving violations found in
these analyses.

Figure 41.
Trail-Making, Part B Distributions and Odd Ratios for
Analyses
Including All Moving Violations, Moving Violations without Speeding,
and Moving Violations without Speeding and Occupant Restraint Citations

Inspection of the OR curves in figure 41 shows the highest values in the middle and bottom
plots. The highest valid OR calculated for this measure, 1.72, was found at the performance level
designated 140 seconds for the analysis of moving violations except speeding. This result was
statistically significant at p<.01 (c2 = 6.70, df=1).

The 140 msec performance level was also the cutpoint at which the percentage of drivers
with moving violations exceeded the percentage of violation-free drivers by the widest margins, for
all three of the analysis categories.

The data plotted in figure 41 are presented in tables 65, 66, and 67 of appendix H. The chi-square test results noted above, with corresponding cell counts, can be found in table 80 of appendix
I.

Dynamic Trails. Figure 42 plots the results for Dynamic Trails. This automated test was
related to the paper-and-pencil Trail-making (Part B) measure but was shorter, with fewer test items,
and also potentially more distracting, with moving traffic in the background instead of a blank page.

With the exception of a spike at the 50-second performance level for the data in the bottom
plot, which represented too few drivers for a valid analysis, the calculated OR value for this measure
hovers near 1.0 across the board. The peak valid OR, 1.27, was found at the 25-second cutpoint in
the bottom plot; this result was not statistically significant (c2 = .24, n.s.). However, there is
convergence in these findings with the (at-fault) crash analysis, which also demonstrated a peak valid
odds ratio at the same cutpoint.

It may again be noted that the sample size (n = 759) for this particular measure was only
about half of that attained for other procedures in the screening battery.

The data plotted in figure 42 are presented in tables 68, 69, and 70 of appendix H. The chi-square test results noted above, with corresponding cell counts, can be found in table 81 of appendix
I.

Scan Test. The remaining measure of perceptual ability, the Scan Test, was a binary
measured scored simply on a pass/fail basis. With only one criterion possible, OR calculation is
irrelevant to cutpoint determination, and no data plot was prepared.

For this measure, 95.6 percent of all drivers in the study sample--whether violation-involved
or not--passed. Whether this was due to the insensitivity of the measurement procedure or whether
these results reflect a true measurement of generally "intact" functional ability is unclear. Either
way, the very small percentage of drivers failing the measure indicates a very limited utility for the
Scan Test as a screening tool.

Of the 81 drivers who failed the Scan Test, only one was convicted of a non-speeding, non-occupant-restraint violation. This result precluded a valid OR calculation, and no chi-square test was
performed for these data.

Rapid Pace Walk. Figure 43 presents the plots for the Rapid Pace Walk measure, the first
of the physical screening tests for which results are reported. As shown, the calculated OR value is
at or below 1.0 except for the highest test completion times in all three plots for this measure.

The peak valid OR, 1.48, was calculated at the performance level designated 5.25 seconds
in the analysis of moving violations except speeding. This result was not statistically significant (c2
= 0.96, n.s.). This same performance level was also where the percentage of drivers with moving
violations exceeded the percentage without violations by the largest amount, in each analysis
category.

The data plotted in figure 43 are presented in tables 71, 72, and 73 of appendix H. The chi-square test results and cell counts can be found in table 82 of appendix I.

Foot Tap. Data plots of the results of the Foot Tap measure are presented in figure 44. As
shown, the odds ratio curves for the top two analysis categories are very close to the dashed
horizontal line (OR = 1.0), indicating no relationship, until the poorest performance levels are
reached. In fact, the peak valid OR of 2.14 is found in the top plot, at the 12.75-s level; this result
approached but failed to reach statistical significance (c2 = 2.34,
n.s.).

In the bottom plot, higher OR values were found, but cell counts were too few for a valid
analysis. Also, the OR values in this plot range from higher to lower as performance shifts from
"intact" to greater and greater degrees of functional loss. This counterintuitive finding is consistent
with the results observed for this measure in the earlier analysis of (at-fault) crashes.

The data plotted in figure 44 are presented in tables 74, 75, and 76 of appendix H. The chi-square test results and corresponding cell counts can be found in table 83 of appendix I.

Head/Neck Rotation. Because only pass/fail outcomes are possible for this (binary) measure,
no odds ratio plot was prepared for the Head/Neck Rotation data. As noted earlier, 81.4 percent of
drivers passed this test. When analyzed to examine the relationship between performance on this
measure and moving violation experience, these data included only three drivers who failed the test
and had at least one non-speeding, non-occupant restraint violation. This result also precluded a
valid calculation of OR, and no statistical tests were performed on these data.

Arm Reach. Results for this remaining measure of physical ability, another binary (pass/fail)
measure, were the most skewed among all screening activities as 99.3 percent passed, and only14
failed this test. Among those who failed, there were no drivers who received convictions for non-speeding, non-occupant-restraint violations. Accordingly, no valid OR calculations were permitted,
and there are no chi-square test results to report.

This section documents costs associated with the functional screening and evaluation
activities undertaken in the Maryland Pilot Older Driver Study. It encompasses administrative and
support activities, as well as the time actually spent by State employees performing the various
testing procedures. The included cost data, as compiled by the MVA, represent the incremental costs
of carrying out the Pilot Study, specifically; the costs associated with medical review of referred
drivers when an activity or procedure was already a part of existing processes at the licensing agency
are accounted for separately. Also, costs associated with the development of materials and
procedures used during driver screening and evaluation by MVA staff are omitted from this
accounting, to the extent that research team members' labor or equipment were covered under this
NHTSA contract or other sources of extramural funding.

After documenting the costs experienced in a research setting to acquire the functional
screening data in the Pilot Study, a projection of the costper licensed driver interacted withby the
MVA to accomplish functional screening in a production setting is presented, consistent with
program parameters provided by the MVA. Supplemental costs associated with post-screening
(education and counseling) activities are similarly estimated.

The cost accounting below is keyed to four categories: labor; equipment; training and quality
control; and overhead. Labor costs include salary, and benefits where applicable, for the staff who
conduct functional testing and who perform program administrative functions such as scheduling,
customer contact, and data management. Equipment costs pertain to hardware and software
resources needed to administer the functional tests. Training and quality control costs cover the time
spent by MVA staff preparing to perform testing activities, and participating in periodic "refresher"
sessions to maintain consistency in the administration of screening procedures. Overhead costs are
limited to the space required to carry out the screening activities, apportioned according to the
amount of time multi-purpose facilities at the MVA were dedicated to these activities.

Because different activities were performed in different venues, cost-per-driver-screened
figures are calculated initially for screening activities performed with license renewal populations,
then modified to account for differential costs in screening medical referral and residential
community populations.

Beginning with functional screening for the license renewal sample in the Pilot Study. the
total number of drivers who participated in screening activities was 2,381. Though only data for
1,876 were complete and valid, the costs described in this section will be based on the total number
of drivers tested during the 11-month interval from the end of November to late in the following
October.

To collect these data, the MVA utilized 7 line personnel who worked three days per week
on this project. This translates to 4.2 full-time employees (FTE). The average hourly wage
including benefits for a line employee is $15.00. Based on a work year of 2,080 hours, the cost for
one FTE was $31,200; thus, the total annual cost for the 4.2 full-time employees who conducted
screening may be estimated at $131,040. Adjusted for an 11-month study period, the resulting labor
cost to acquire screening data for the license renewal sample was $120,120.

Administrative and logistics support for this data collection activity was provided by two
research associates in the MVA Driver Safety Research Office, who devoted approximately one-third
of their time each. At an hourly rate of $33.00, this resulted in an additional 0.66 FTE at an annual
cost of $45,760. The adjusted figure for the 11-month duration of the Pilot Study is $41,947. Thus,
total labor costs to perform functional screening for the license renewal sample in the Pilot Study
may be estimated at $162,067.

The costs of equipment dedicated to screening activities in the Pilot Study were confined to
additional computers (PC's) and peripheral devices (light pens and scanners), plus materials used for
"manual" data collection (e.g., test stimuli and scoring forms). Specifically, three (3) PC's were
purchased at $843 each, and subsequently were connected to a wide area network for data acquisition
and data entry. Three (3) light pens were purchased at a cost of $258 each, to acquire data for
measures where examinees actually needed to touch the screen to indicate their responses. And, two
(2) CCD scanners used to read the bar codes on driver's licenses containing their driver identification
(Soundex) numbers were purchased, at a cost of $198 each. Seven (7) test kits containing all
materials and supplies used to perform the "Gross Impairment Screening" (GRIMPS) measures were
also purchased, at a cost of $100 each. Total costs for equipment and supplies therefore may be
estimated at $4,399.

Estimated costs associated with training and quality control may be derived based on the time
that MVA staff who collected screening data and performed administrative and support functions
were engaged in these activities. An initial training exercise spanning two, half-day (4-hour)
sessions included ten (10) MVA line personnel and two (2) MVA research associates. For two days
following initial training, ten (10) additional line personnel provided on-site supervision and
observation of the individuals collecting screening data, for 6 hours each day. Through the duration
of the Pilot Study, periodic visits for observation and "refresher" training to promote consistency and
reduce errors in data collection and data entry procedures required a total of 12 full days of staff time
at the research associate level. Together, these activities required the equivalent of 200 hours of time
for line personnel, at $15/hr, plus 112 hours of research associate time at $33/hr, for a total of
$6,696.

Finally, the real estate required to collect screening data for license renewal drivers in the
Pilot Study consisted of a room in each of three MVA field offices. The rooms, which were used
for other MVA functions when screening activities were not being performed, provided a footprint
of approximately 100 square feet. At a fair market value of $12/ft²/year, the cost of this space
utilized full-time, would be $3,600. Utilized three days per week, the apportioned cost of MVA
office space to conduct screening was 60 percent of this amount, or $2,160.

Summing the component costs identified above associated with Pilot Study efforts to acquire
the functional abilities screening data, enter and store the data, and generate raw data tables to
support the project analyses, for a sample of license renewal drivers tested at MVA field offices
yields an estimated total cost of $175,322.

A preliminary estimate of the cost-per-driver-screened in the research settings of the
Maryland Pilot Older Driver Study is reached by dividing this amount by the number of licensed
drivers tested by the MVA under this program-2,381. The result is $73.63. This estimate is termed
"preliminary" because, according to an MVA research associate,3 the amount of time devoted to data
collection, per se, averaged no more than 30 minutes per driver. The apparently much larger time
requirement suggested by the 4.2 FTE figure cited above reflects a number of factors, most
prominently challenges in recruiting the study sample: only older individuals were approached to be
asked to volunteer for the license renewal study, and only about half of those approached agreed to
participate.

A first step toward developing an estimate of the cost-per-driver-screened in a production
setting versus the research setting is reached by limiting the time allowed per driver to only the 30
minutes (or less) that is necessary to acquire functional screening data. Because this activity would
no longer be voluntary, many of the extra duties experienced by the MVA staff in the research setting
would disappear. With this one adjustment, the cost element represented by the line personnel
serving as data collectors in the Pilot Study is reduced to 1,191 hours (i.e., the number needed to
screen the license renewal sample at one half-hour per driver) times the hourly wage of $15.00, or
$17,865. Including equipment, training and quality control, and overhead costs as previously
documented, the adjusted total cost is $31,120, or $13.07 per driver screened.

Next, certain cost elements were modified and others were added as data collection moved
into other venues during the Pilot Study. Principal differences were the use of Driver License
Examiners (DLE's) instead of line personnel to conduct screening for the medical referral sample;
and, the addition of occupational therapists to provide feedback and counseling to drivers on the
meaning of their screening results and changes in driving habits they should consider, with the
residential community sample.

The DLE staff who performed functional screening of the medical referral sample earned a
wage (salary plus benefits) of $20 per hour. The introduction of staff at this level followed observation of inconsistencies in test administration during Pilot Study data collection with the
license renewal sample. The DLE staff, who were accustomed to performing a wide range of
examination activities, did achieve a higher degree of consistency in administering the functional
tests. In addition, because the medically-referred drivers were screened only during scheduled
appointments, the test administration time was effectively limited to and consistently fell within the
range of 20 to 30 minutes per driver, as stipulated above.

If all functions performed by line personnel in the cost estimate developed above--including
training and quality control as well as data collection--are instead performed by DLE-level staff, the
adjusted total cost for functional screening including equipment, training and quality control, and
overhead increases to $38,075, or an estimate of $15.99 per driver screened.

Finally, when older drivers in the residential community sample were screened in the Pilot
Study, an occupational therapist (OT) was available to provide feedback and counseling services.
By design, these interactions were to be tailored as follows: functionally intact drivers would receive
educational information about the relationship between functional ability and driving risk, advice
on self-testing and what to do when abilities begin to decline in the future; while persons "failing"
one or more screening measures, in addition to receiving educational information, would be
counseled on specific risks posed by their functional impairments and/or what actions were needed
vis-à-vis changes in driving habits, where they should go for more in-depth assessment, and what
options might be explored to remediate their functional loss. As a practical matter, however, the
OT's time was limited to interactions with drivers for whom the screening activities indicated the
most pronounced functional deficits. The occupational therapists participating in the Pilot Study
were outside consultants, i.e., not MVA staff personnel, who were paid $45 per hour.

If OT's, nurses, or similarly-qualified professionals were engaged to provide counseling
services on a broader scale, the incremental cost associated with this service would be driven by the
percentage of drivers screened who would "fail" the functional ability screening, and the fraction of
this group who would require one-on-one attention from a medical professional to have their
questions answered or to receive the necessary referrals for further evaluation and/or to identify
remediation options.

It is the perspective of MVA officials4 that not more than 25 percent of the population of
renewing drivers in the 55+ cohort would "fail" functional screening using a to-be-selected subset
of the measures examined in the Pilot Study, and applying the cutpoints that are best supported by
available data relating functional status to safety outcomes as per the analyses reported herein; and
further, that a majority of even the "failing" drivers could have their needs for feedback and
counseling effectively met by properly trained DLE-level staff. Only those individuals whose
questions could not be answered adequately or whose need for an immediate referral required the
attention or action of a medical professional would interact with an OT or nurse after completing
screening. Accordingly, incremental cost estimates for the provision of post-screening services to
the license renewal sample, in a production setting, are based on the following assumptions:

Post-screening feedback for all of the "functionally intact" drivers (75 percent of the total
number screened) would be accommodated through interactions with the DLE that focus on
education and promote awareness of the functional abilities needed for safe driving, at 5
minutes per interaction;

Eighty percent of drivers with significant functional loss (20 percent of the total number
screened) would be accommodated through more extensive interactions with the DLE, at 10
minutes per interaction; and

Twenty percent of drivers with significant functional loss (5 percent of the total number
screened) would receive initial feedback from the DLE, lasting up to 10 minutes, then would
require additional consultation with a medical professional, at 20 minutes per interaction.

Based on the $20/hr and $45/hr costs experienced in the Pilot Study for DLE and OT
labor, respectively, these assumptions yield an incremental cost of $6,745, raising the total
cost for screening and evaluation activities in a license renewal context to $44,819 and the
cost-per-driver interacted with by the MVA to $18.82.

It deserves mention that no costs have been included in these estimates for Pilot
Study involvement by the Chief or the Daily Duty Doctors serving on the Medical Advisory
Board at the MVA. While these individuals played key roles in the early planning and later
evaluation of screening activities, an ongoing screening program is viewed as but one
additional source of information complementing other data currently considered in medical
reviews for fitness-to-drive determinations. Since fitness-to-drive determinations are a
defining characteristic of the MAB, the only incremental cost in this process is represented
by the acquisition of screening data plus whatever post-screening educational and counseling
services, if any, are provided to drivers. The consideration of screening outcomes within the
context of responsibilities normally discharged by the MAB, by comparison, does not
represent an incremental cost.

Perhaps more importantly, it must be emphasized that the cost analysis in this section
reflects screening activities (including data entry) that were performed mostly on a manual
and labor-intensive basis--only two of the measures were automated--and by MVA staff
for whom this was a completely novel assignment. As with any procedure, staff became
more efficient and skilled in administering the functional tests with experience, especially
the Driver License Examiners.

Most important from a cost standpoint is the potential to automate the majority of the
most-promising measures emerging from the Pilot Study. Automation of data entry as well
as data collection functions could enable one staff member to direct and monitor the
screening of two or perhaps three drivers, and still provide feedback within the parameters
outlined above. Under this scenario, the cost-per-driver-screened could be reduced to the
range of $5 to $10.

Further discussion relating the cost estimates developed above to the anticipated
benefits of a functional capacity screening program to identify persons at high risk of driving
impairment is presented in Volume 1 of this report.